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Related Experiment Video

Updated: May 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Action-factorized Rainbow deep Q-network with token Transformer for computation offloading in edge computing-enabled

Shengtian Zhang^1,2, Haolin Yang¹, Hyeonseok Kim¹

¹Department of Artificial Intelligence Convergence, Pukyong National University, Busan, South Korea.

|May 11, 2026

Summary

This study introduces an advanced deep reinforcement learning algorithm for effective computation offloading in edge computing within the Internet of Ships. The developed strategy significantly optimizes latency and energy consumption for maritime digitalization.

Related Experiment Videos

Last Updated: May 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Maritime technology
Computer science
Artificial intelligence

Background:

Edge computing (EC) in the Internet of Ships (IoS) offers reduced latency and energy consumption compared to cloud architectures.
Effective computation offloading is crucial for realizing EC benefits but is challenging in dynamic maritime environments due to complex decision spaces and variable wireless channels.

Purpose of the Study:

To propose a deep reinforcement learning (DRL) algorithm for discovering efficient computation offloading strategies in EC-enabled IoS (EC-IoS).
To address the challenges of high-dimensional decision spaces, system constraints, and dynamic maritime wireless channels.

Main Methods:

Development of an action-factorized Rainbow deep Q-network (DQN) incorporating a token Transformer.
Custom token Transformer-based state and action encoders to manage complex decision spaces.
Acceleration using a parallel training architecture for improved learning efficiency and stability.

Main Results:

The proposed algorithm's learned computation offloading strategies significantly outperform baseline methods on the weighted latency-energy objective.
Achieved a zero rate of invalid actions, ensuring all system constraints are met and practical feasibility.
Demonstrated robust performance in balancing latency and energy consumption.

Conclusions:

The action-factorized Rainbow DQN with token Transformer provides a robust solution for computation offloading in EC-IoS.
The algorithm effectively supports maritime digitalization and automation by optimizing performance and ensuring practical implementation.
Highlights the potential of DRL in solving complex optimization problems in maritime edge computing environments.